{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(x):\n",
    "    return 1/(1 + np.exp(-x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(-8,8,0.2)\n",
    "plt.plot(x, sigmoid(x))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid_derivative(x):\n",
    "    y = sigmoid(x) * (1 - sigmoid(x))\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,sigmoid_derivative(x))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tanh(x):\n",
    "    y = (np.exp(x) - np.exp(-x)) / (np.exp(x) + np.exp(-x))\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, tanh(x))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Relu(x):\n",
    "    return np.where(x < 0,0,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, Relu(x))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
